https://github.com/mohab-sameh/anomaly-based-ids-workbench

The ultimate workbench for research & development of AI-powered anomaly-based Intrusion Detection Systems (IDS)
https://github.com/mohab-sameh/anomaly-based-ids-workbench

deep-learning intrusion-detection intrusion-detection-system machine-learning security

Last synced: 13 days ago
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The ultimate workbench for research & development of AI-powered anomaly-based Intrusion Detection Systems (IDS)

• Host: GitHub
• URL: https://github.com/mohab-sameh/anomaly-based-ids-workbench
• Owner: mohab-sameh
• License: gpl-3.0
• Created: 2020-10-11T17:16:40.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-07-05T16:06:16.000Z (over 2 years ago)
• Last Synced: 2024-10-10T17:01:50.071Z (26 days ago)
• Topics: deep-learning, intrusion-detection, intrusion-detection-system, machine-learning, security
• Language: Jupyter Notebook
• Homepage:
• Size: 73.6 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
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 Anomaly-Based Intrusion Detection Workbench 🔍


This is a workbench for the research and development of Anomaly-Based Intrusion Detection Systems.




Demo






Some Features 📋


* Easily develop complete & usable machine learning and deep learning pipelines 🧠

* Utilize 3rd Party Datasets (such as NSL-KDD, KDD-99, ISCX-NBXX) 📊

* Connect and import CSV datasets through your AWS S3 buckets 🗃️

* Perform Live Packet Capture & predict network attacks using your developed ML/DL Model! ☢️🔍

* Export comparative Metrics of executed pipelines 📑

* Simple and Intuitive GUI 🖥️

* Cloud-Deployable ☁️

* Tons of Data exploration, preprocessing, machine learning, and deep learning tools! 💻

* Cross-Platform usability 💻📱🖥️




Tested Platforms 🖥️


* Deployed on Windows 10 (20H2), Mac OS 10.14, Ubuntu 18.04/20.04

* Access through any device with your browser of choice (tested on Firefox, Safari, MS Edge, Chrome, Opera).




Installation 📜


* Install requirements:

```

pip install requirements.txt

```




Usage⌨️


* Run app:

```

streamlit run app.py

```

* Use through your browser of choice. 

* Or Try a ready cloud-deployed instance [here](https://share.streamlit.io/mohab-sameh/anomaly-based-ids-workbench/main/Implementation/app-files/app.py)




Packet Capture Dependencies 🔍


* Libpcap:

```

pip install libpcap-dev

```

* GCC ([installation instructions](https://linuxize.com/post/how-to-install-gcc-compiler-on-ubuntu-18-04/))

* KDD Feature extractor ([repo](https://github.com/AI-IDS/kdd99_feature_extractor) or use my [prebuilt repo](https://github.com/mohab-sameh/Kdd99-Feature-Extractor-Prebuilt))

note: please make sure the KDD Feature extractor is in the root directory (ex: ~/Kdd99-Feature-Extractor-Prebuilt/kdd99_feature_extractor-master)




 

  Published literature:



[M. S. Abdel-Wahab, A. M. Neil and A. Atia, "A Comparative Study of Machine Learning and Deep Learning in Network Anomaly-Based Intrusion Detection Systems," 2020 15th International Conference on Computer Engineering and Systems (ICCES), 2020, pp. 1-6, doi: 10.1109/ICCES51560.2020.9334553.](https://ieeexplore.ieee.org/document/9334553)